Brian DeCost

4.6k total citations · 2 hit papers
46 papers, 2.9k citations indexed

About

Brian DeCost is a scholar working on Materials Chemistry, Mechanical Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Brian DeCost has authored 46 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Materials Chemistry, 12 papers in Mechanical Engineering and 11 papers in Computational Theory and Mathematics. Recurrent topics in Brian DeCost's work include Machine Learning in Materials Science (32 papers), X-ray Diffraction in Crystallography (11 papers) and Computational Drug Discovery Methods (10 papers). Brian DeCost is often cited by papers focused on Machine Learning in Materials Science (32 papers), X-ray Diffraction in Crystallography (11 papers) and Computational Drug Discovery Methods (10 papers). Brian DeCost collaborates with scholars based in United States, Canada and Singapore. Brian DeCost's co-authors include Kamal Choudhary, Elizabeth A. Holm, Francesca Tavazza, Toby Francis, Ankit Agrawal, Alok Choudhary, Chris Wolverton, Simon J. L. Billinge, Chi Chen and Anubhav Jain and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Brian DeCost

45 papers receiving 2.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Recent advances and applications of deep learning methods in materials science

2022 652 citations Kamal Choudhary, Brian DeCost et al. npj Computational Materials profile →
Atomistic Line Graph Neural Network for improved materials property predictions

2021 415 citations Kamal Choudhary, Brian DeCost npj Computational Materials profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Brian DeCost United States	20	1.9k	615	503	346	297	46	2.9k
Kamal Choudhary United States	31	3.1k 1.6×	441 0.7×	806 1.6×	486 1.4×	456 1.5×	101	4.1k
Logan Ward United States	24	3.5k 1.8×	930 1.5×	867 1.7×	792 2.3×	351 1.2×	77	4.7k
Jonathan Schmidt Germany	18	2.0k 1.0×	344 0.6×	699 1.4×	334 1.0×	273 0.9×	33	3.1k
Francesca Tavazza United States	31	2.6k 1.4×	411 0.7×	934 1.9×	315 0.9×	420 1.4×	81	3.7k
Rohit Batra United States	28	3.2k 1.6×	478 0.8×	1.3k 2.5×	793 2.3×	504 1.7×	54	4.6k
Chiho Kim United States	28	3.2k 1.6×	584 0.9×	1.2k 2.5×	900 2.6×	646 2.2×	65	4.6k
Taylor D. Sparks United States	31	3.0k 1.6×	512 0.8×	1.1k 2.2×	406 1.2×	339 1.1×	112	4.0k
Arun Mannodi‐Kanakkithodi United States	23	2.4k 1.2×	431 0.7×	934 1.9×	545 1.6×	593 2.0×	63	3.1k
Daniel W. Davies United Kingdom	17	2.6k 1.3×	386 0.6×	810 1.6×	693 2.0×	393 1.3×	49	3.9k

Countries citing papers authored by Brian DeCost

Since Specialization

Citations

This map shows the geographic impact of Brian DeCost's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Brian DeCost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian DeCost more than expected).

Fields of papers citing papers by Brian DeCost

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian DeCost. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Brian DeCost. The network helps show where Brian DeCost may publish in the future.

Co-authorship network of co-authors of Brian DeCost

This figure shows the co-authorship network connecting the top 25 collaborators of Brian DeCost. A scholar is included among the top collaborators of Brian DeCost based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Brian DeCost. Brian DeCost is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Anber, Elaf A., James L. Hart, Howie Joress, et al.. (2025). Influence of short-range order on precipitate orientation relationships in aluminum containing FCC high entropy alloys. Intermetallics. 184. 108832–108832.

Li, Kangming, Andre Niyongabo Rubungo, X. L. Lei, et al.. (2025). Probing out-of-distribution generalization in machine learning for materials. Communications Materials. 6(1). 17 indexed citations

Li, Kangming, Brian DeCost, Kamal Choudhary, & Jason Hattrick‐Simpers. (2024). A reproducibility study of atomistic line graph neural networks for materials property prediction. Digital Discovery. 3(6). 1123–1129. 1 indexed citations

Kusne, A. Gilad, Austin McDannald, & Brian DeCost. (2024). Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning. Digital Discovery. 3(11). 2211–2225. 2 indexed citations

Gupta, Vishu, Kamal Choudhary, Brian DeCost, et al.. (2024). Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets. npj Computational Materials. 10(1). 44 indexed citations

Rahman, Md. Habibur, Satyesh Kumar Yadav, Ghanshyam Pilania, et al.. (2024). Accelerating defect predictions in semiconductors using graph neural networks. SHILAP Revista de lepidopterología. 2(1). 21 indexed citations

Choudhary, Kamal, et al.. (2023). Unified graph neural network force-field for the periodic table: solid state applications. Digital Discovery. 2(2). 346–355. 72 indexed citations

Wines, Daniel, Ramya Gurunathan, Kevin F. Garrity, et al.. (2023). Recent progress in the JARVIS infrastructure for next-generation data-driven materials design. Applied Physics Reviews. 10(4). 23 indexed citations

Zhang, Runze, et al.. (2023). Editors’ Choice—AutoEIS: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy. Journal of The Electrochemical Society. 170(8). 86502–86502. 17 indexed citations

10.

Maffettone, Phillip M., Daniel Allan, Stuart I. Campbell, et al.. (2023). Self-driving multimodal studies at user facilities. Acta Crystallographica Section A Foundations and Advances. 79(a1). a325–a325. 1 indexed citations

11.

Carbone, Matthew R., Hyeong Jin Kim, Shinjae Yoo, et al.. (2023). Flexible formulation of value for experiment interpretation and design. Matter. 7(2). 685–696. 4 indexed citations

12.

Li, Kangming, Brian DeCost, Kamal Choudhary, Michael Greenwood, & Jason Hattrick‐Simpers. (2023). A critical examination of robustness and generalizability of machine learning prediction of materials properties. npj Computational Materials. 9(1). 68 indexed citations

13.

Blades, William, Elaf A. Anber, Brian DeCost, et al.. (2023). An Experimental High-Throughput to High-Fidelity Study Towards Discovering Al–Cr Containing Corrosion-Resistant Compositionally Complex Alloys. 1(2). 336–353. 13 indexed citations

14.

McDannald, Austin, Howie Joress, Brian DeCost, et al.. (2022). Reproducible sorbent materials foundry for carbon capture at scale. Cell Reports Physical Science. 3(10). 101063–101063. 1 indexed citations

15.

Audus, Debra J., Austin McDannald, & Brian DeCost. (2022). Leveraging Theory for Enhanced Machine Learning. ACS Macro Letters. 11(9). 1117–1122. 18 indexed citations

16.

Choudhary, Kamal, Brian DeCost, Chi Chen, et al.. (2022). Recent advances and applications of deep learning methods in materials science. npj Computational Materials. 8(1). 652 indexed citations breakdown →

17.

Choudhary, Kamal & Brian DeCost. (2021). Atomistic Line Graph Neural Network for improved materials property predictions. npj Computational Materials. 7(1). 415 indexed citations breakdown →

18.

Oviedo, Felipe, Zekun Ren, Shijing Sun, et al.. (2018). Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks.. arXiv (Cornell University). 6 indexed citations

19.

Choudhary, Kamal, Brian DeCost, & Francesca Tavazza. (2018). Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape. Physical Review Materials. 2(8). 118 indexed citations

20.

DeCost, Brian & Elizabeth A. Holm. (2016). A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures. Data in Brief. 9. 727–731. 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact